Get Certified by IIIT-Bhagalpur as an

The primary method of instruction is virtual instructor-led training lectures by eminent faculty & industry professionals. These Live-Tutored sessions and not recorded sessions will be beamed online via the internet to the participant’s desktop/ laptop or classrooms. All enrolled participants will also, be provided access to other learning aids, reference materials, assessments, and hands-on workshops as appropriate. Throughout the duration of the course, students will have the flexibility to reach out to the faculty, real-time during the LIVE lectures or offline to raise questions and clear their queries.
At the end of the course, students will also be allocated Project work that is designed to provide adequate practical and hands-on experience in implementing the concepts learned during the course

Duration: 150 Hours Session: 2 hours/day - 2 days a week (weekend) Batch Size: 50 Students

Live Modules

Hours of Learning

Interactive Sessions

Capstone Projects

This Vitti & IIIT-BHAGALPUR Certified course is focused on building industry-ready “Artificial Intelligence Analyst” & “Data Scientist” who can work on machine learning, data mining, and statistical modeling for predictive and prescriptive enterprise analytics. This program will enable you to develop deep understanding and experience with machine learning and data analysis. Familiarity with common tools for data management and analysis including machine learning can be applied on real world problems for building predictive models using machine learning on your own.
All enrolled participants will be provided access to other learning aids, reference materials, assessments, and hands-on workshops as appropriate. During the course students will also be allocated Project work that is designed to provide adequate practical and hands on experience in implementing the concepts learned during the course.

Syllabus Structure

What you will learn from this course

Module 1 : Introduction to AI
  • Rise of Artificial Intelligence
Module 2 : Python Programming Basics
  • Introduction to Python Programming
  • Packages in Python
  • Data Types and Operations
  • Statements and Syntax in Python
  • File Operations
  • Database Connection
  • Industry Talk
Module 3 : Data, Knowledge and Machine Learning with Python
  • Introduction to Machine Learning & Artificial Intelligence
  • Building Intelligent Machines to Transform Data into Knowledge
  • Applications of AI
  • Data Pre-processing, Dealing with Missing Data, Handling Categorical Data, Data Normalization
  • Data Visualization
  • Probability Theory, Probabilistic Learning in Python, Probability Distribution
  • The Naive Bayes Algorithm, Industry Talk, and Statistical Analysis
  • Unsupervised Learning, Hierarchical Clustering
  • Self Organizing Maps, K means Clustering
  • Practical Learning
  • Supervised Learning & Classification
  • Naïve Bayes Classifier
  • Regression Analysis
  • K Nearest Neighbour Algorithm
  • Support Vector Machine
  • Artificial Neural Network
  • Reinforcement Learning, Exercise Using Python
  • Deep Learning in Python, Convolutional Neural Network
  • Multilayer Neural Network, Industry Talk
Module 4 : Artificial Intelligence with Python
  • Logic Programming
  • Heuristic Search
  • Building Games with AI
  • Industry Talk

Module 5 : AI Landscape
  • AI Impact in the world today.
  • History and Evolution of AI
  • AI Explained
  • AI Technologies
  • Summary & Resources
Module 6 : AI Industry Adoption Approaches
  • AI Industry Impact
  • Autonomous Vehicles
  • Smart Robotics
  • Future workforce & AI
  • Summary & Resources
Module 7 : Natural Language Understanding
  • NLP Overview
  • NLP Explained
  • Virtual Agents Overview
  • Virtual Agents for the Enterprise
  • Summary & Resources
Module 8 : Computer Vision
  • Computer Vision Overview
  • AI Vision through Deep Learning
  • Computer Vision for the Enterprise
  • Experiments
  • Summary & Resources
Module 9 : Machine Learning & Deep Learning
  • Machine Learning Explained
  • Deep Learning Explained
  • Deep Learning Ecosystem
  • Experiments
  • Summary & Resources
Module 10 : Future Trends for AI
  • Artificial Intelligence Trends
  • Limits of Machine and Human
  • AI predictions in the next 5 years
  • Summary & Resources
Module 11 : Industry Projects
  • 4 Industry Projects

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Our Faculty

A.K Sinha
Lead Instructor


One of the excellent courses in deep learning. As stated its advanced and enjoyed a lot in solving the assignments. Looking forward for more such courses especially in Natural language processing.

- Pradeep Bhatt

The hardest, yet most satisfying course I've ever taken in deep learning, by the end of the course I was doing stuff that was borderline sci-fi and that was just "introduction" to deep learning

- Swati Joshi

Great course. Even though I've done Andrew Ng's ML course twice and completed his Deep Learning Specialization, I learned a lot of new things in this Intro Course of AML specialization.

- Shivam Pandey

Course FAQ

AI-Analyst is an invaluable intelligence-driven tool that is used to massively expand the coverage of cyber analysis in the era of applied threat intelligence.
Candidate could be a graduate with degree like Bsc, BCA, MCA, B.E or B.Tech and must have studied PCM in 10+2.
Yes, anyone who is passionate about learning AI can enroll in this program.
Specific Jobs in AI:
  • Machine Learning Researchers
  • AI Engineer
  • Data Mining and Analysis
  • Machine Learning Engineer
  • Data Scientista
  • Business Intelligence (BI) Developer
Students can pursue data science courses after passing class 10+2. For admissions in UG Data Science Courses, 50% marks in class 12th whereas for PG Data Science Courses, 50% marks in graduation in engineering or related streams are required.
On completion of the program, students will have developed a world-class skill set in their selected technology domain that provides “Employability Enhancing” skills and practical exposure thereby substantially increasing their earning potential and compensation benchmarks.
Demonstrate fundamental understanding of the history of artificial intelligence (AI) and its foundations.
Apply basic principles of AI in solutions that require problem solving, inference, perception, knowledge representation, and learning.
Demonstrate awareness and a fundamental understanding of various applications of AI techniques in intelligent agents, expert systems, artificial neural networks and other machine learning models.
Demonstrate proficiency developing applications in an 'AI language', expert system shell, or data mining tool.
Demonstrate proficiency in applying scientific method to models of machine learning.
Demonstrate an ability to share in discussions of AI, its current scope and limitations, and societal implications.
Hey, No need to worry! Our in-house Learning Management System will provide you with recordings of every lecture.
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